Learn Time Series Analysis with Python
Learn the fundamentals of Time Series Analysis with Python.
Updated on Apr, 2024
Language - English
Data Analysis,IT & Software,Python
Lectures -75
Resources -1
Duration -7.5 hours
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Course Description
Time Series Analysis with Python course will help you learn to work with a number of Python libraries, providing you with complete training. You will use the powerful time-series functionality built into pandas, as well as other fundamental libraries such as NumPy, matplotlib, statsmodels, Sklearn, and ARCH.
Time Series Analysis with Python Overview
The course starts with programming in Python which is the essential skill required and then we will explore the fundamental time series theory to help you understand the modeling that comes afterward.
Time series analysis and forecasting is one of the areas of Data Science and has a wide variety of applications in the industries in the current world. Many industries looking for a Data Scientist with these skills.
This course covers all types of modeling techniques for forecasting and analysis. With these tools, we will master the most widely used models out there:
Additive Model
Multiplicative Model
AR (autoregressive model)
Simple Moving Average
Weighted Moving Average
Exponential Moving Average
ARMA (autoregressive-moving-average model)
ARIMA (autoregressive integrated moving average model)
Auto ARIMA
Goals
What will you learn in this course:
Master basic to advanced Time Series methods.
Learn auto-regressive methods,
Learn Time Series Visualization in Python.
ARMA, ARIMA, SARIMA in Python.
ACF and PACF.
Auto ARIMA in Python.
Additive Model
Multiplicative Model
AR (autoregressive model)
Simple Moving Average
Weighted Moving Average
Exponential Moving Average
Prerequisites
What are the prerequisites for this course?
Basic knowledge of Statistics.
Basic understanding of Python.
Should have a Gmail Account and should be able to open Google Drive.
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
4 Lectures
-
Introduction
04:09
04:09
-
What is time series data
02:38
02:38
-
Components of Time Series
03:37
03:37
-
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Setting Up Course
2 Lectures
Time Series Visualization
8 Lectures
Linear Regression Intuition
9 Lectures
Time Series Forecasting with Linear Regression
5 Lectures
Additive Time Series Model
6 Lectures
Multiplicative Time Series Model
7 Lectures
Auto Regressive Methods
8 Lectures
Smoothing Methods (Moving Average)
10 Lectures
Non Seasonal ARIMA models
13 Lectures
Auto ARIMA
3 Lectures
Instructor Details
Srikanth Guskra
Data ScientistHi,
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